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ClickHouse

Compatibility

仅在 Node.js 上可用。

¥Only available on Node.js.

ClickHouse 是一个强大的开源列式数据库，用于处理分析查询和高效存储。ClickHouse 旨在提供强大的向量搜索和分析组合。

¥ClickHouse is a robust and open-source columnar database that is used for handling analytical queries and efficient storage, ClickHouse is designed to provide a powerful combination of vector search and analytics.

设置​

¥Setup

  1. 启动 ClickHouse 集群。有关详细信息，请参阅 ClickHouse 安装指南。

    ¥Launch a ClickHouse cluster. Refer to the ClickHouse Installation Guide for details.

  2. 启动 ClickHouse 集群后，从集群的 Actions 菜单检索 Connection Details。你需要主机、端口、用户名和密码。

    ¥After launching a ClickHouse cluster, retrieve the Connection Details from the cluster's Actions menu. You will need the host, port, username, and password.

  3. 在你的工作区中安装 ClickHouse 所需的 Node.js 对等依赖。

    ¥Install the required Node.js peer dependency for ClickHouse in your workspace.

你需要安装以下对等依赖：

¥You will need to install the following peer dependencies:

npm install -S @clickhouse/client mysql2
tip

请参阅 此部分包含有关安装集成包的一般说明。

¥See this section for general instructions on installing integration packages.

npm install @langchain/openai @langchain/community @langchain/core

索引和查询文档​

¥Index and Query Docs

import { ClickHouseStore } from "@langchain/community/vectorstores/clickhouse";
import { OpenAIEmbeddings } from "@langchain/openai";

// Initialize ClickHouse store from texts
const vectorStore = await ClickHouseStore.fromTexts(
  ["Hello world", "Bye bye", "hello nice world"],
  [
    { id: 2, name: "2" },
    { id: 1, name: "1" },
    { id: 3, name: "3" },
  ],
  new OpenAIEmbeddings(),
  {
    host: process.env.CLICKHOUSE_HOST || "localhost",
    port: process.env.CLICKHOUSE_PORT || 8443,
    username: process.env.CLICKHOUSE_USER || "username",
    password: process.env.CLICKHOUSE_PASSWORD || "password",
    database: process.env.CLICKHOUSE_DATABASE || "default",
    table: process.env.CLICKHOUSE_TABLE || "vector_table",
  }
);

// Sleep 1 second to ensure that the search occurs after the successful insertion of data.
// eslint-disable-next-line no-promise-executor-return
await new Promise((resolve) => setTimeout(resolve, 1000));

// Perform similarity search without filtering
const results = await vectorStore.similaritySearch("hello world", 1);
console.log(results);

// Perform similarity search with filtering
const filteredResults = await vectorStore.similaritySearch("hello world", 1, {
  whereStr: "metadata.name = '1'",
});
console.log(filteredResults);

API Reference:

从现有集合中查询文档​

¥Query Docs From an Existing Collection

import { ClickHouseStore } from "@langchain/community/vectorstores/clickhouse";
import { OpenAIEmbeddings } from "@langchain/openai";

// Initialize ClickHouse store
const vectorStore = await ClickHouseStore.fromExistingIndex(
  new OpenAIEmbeddings(),
  {
    host: process.env.CLICKHOUSE_HOST || "localhost",
    port: process.env.CLICKHOUSE_PORT || 8443,
    username: process.env.CLICKHOUSE_USER || "username",
    password: process.env.CLICKHOUSE_PASSWORD || "password",
    database: process.env.CLICKHOUSE_DATABASE || "default",
    table: process.env.CLICKHOUSE_TABLE || "vector_table",
  }
);

// Sleep 1 second to ensure that the search occurs after the successful insertion of data.
// eslint-disable-next-line no-promise-executor-return
await new Promise((resolve) => setTimeout(resolve, 1000));

// Perform similarity search without filtering
const results = await vectorStore.similaritySearch("hello world", 1);
console.log(results);

// Perform similarity search with filtering
const filteredResults = await vectorStore.similaritySearch("hello world", 1, {
  whereStr: "metadata.name = '1'",
});
console.log(filteredResults);

API Reference:

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